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Applicability of Hadamard relaxation method to MMW and THz Imaging with compressive sensing

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Abstract

Compressive sensing (CS) is widely considered a promising method for millimeter wave (MMW) and terahertz (THz) imaging, especially in security screening. In many real-life application scenarios, a CS reconstruction algorithm has to be simultaneously robust, noise-tolerant and fast to be of practical use. However, a lot of CS reconstruction algorithms are not designed aiming at such overall performance, preventing them to become applicable in commercial imaging systems. Having investigated some CS algorithms, we find that Hadamard relaxation method is a potential candidate for commercial CS imaging. By using MATLAB, we study Hadamard relaxation method focusing on its under-sampling ratio, tolerance to noise and efficiency. Comparisons with several other CS algorithms are made using the available data in references. The results demonstrate that the overall performance of Hadamard relaxation method is among the best for real-life and real-time applications of MMW and THz imaging.

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References

  1. Kemp, M.C., Taday, P.F., Cole, B.E., Cluff, J.A., Fitzgerald, A.J., Tribe, W.R.: Security applications of terahertz technology. Proc. SPIE 5070, 44–52 (2003)

    Article  Google Scholar 

  2. Redo-Sanchez, A., Laman, N., Schulkin, B., Tongue, T.: Millimeter, and terahertz waves. J. Infrared 34, 500–518 (2013)

    Article  Google Scholar 

  3. Chen, J., Chen, Y.Q., Zhao, H.W., Bastiaans, G.J., Zhang, X.C.: Absorption coefficients of selected explosives and related compounds in the range of 0.1–2.8 THz. Opt. Express 15, 12060–12067 (2007)

    Article  Google Scholar 

  4. Zhong, H., Redo-sanchez, A., Zhang, X.C.: Standoff sensing and imaging of explosive related chemical and bio-chemical materials using THz–TDS. Int. J. High Speed Electron. Syst. 17, 239–249 (2007)

    Article  Google Scholar 

  5. Liu, H.B., Zhong, H., Karpowicz, N., Chen, Y.Q., Zhang, X.C.: Terahertz spectroscopy and imaging for defense and security applications. Proc. IEEE 95, 1514–1527 (2007)

    Article  Google Scholar 

  6. Fukunaga, K., Hosako, I.: Innovative non-invasive analysis techniques for cultural heritage using terahertz technology. C. R. Phys. 11, 519–526 (2010)

    Article  Google Scholar 

  7. Siles, G.A., Riera, J.M., Garcia-del-Pino, P., Romeu, J.: Atmospheric propagation at 100 and 300 GHz: assessment of a method to identify rainy conditions during radiosoundings. Prog. Electromagn. Res. 130, 257–259 (2012)

    Article  Google Scholar 

  8. Zimdars, D., Valdmanis, J.A., White, J.S., Stuk, G., Williamson, S., Winfree, W.P., Madaras, E.I.: Technology and applications of terahertz imaging non-destructive examination: inspection of space shuttle sprayed on foam insulation. AIP Conf. Proc. 760, 570–577 (2005)

    Article  Google Scholar 

  9. http://www.sds.l-3com.com/advancedimaging/safeview.htm

  10. http://news.xinhuanet.com/fortune/2014-05/14/c_126498364.htm

  11. Cooper, K.B., Dengler, R.J., Llombart, N., Talukder, A., Panangadan, A.V., Peay, C.S., Mehdia, I., Siegel, P.H.: Fast, high-resolution terahertz radar imaging at 25 meters. Proc. SPIE 7671, 76710Y (2010)

    Article  Google Scholar 

  12. Sato, H., Sawaya, K., Mizuno, K., Uemura, J., Takeda, M., Takahashi, J., Yamada, K., Morichika, K., Hasegawa, T., Hirai, H., Niikura, H., Matsuzaki, T., Kato, S., Nakada, J.: Passive millimeter-wave imaging for security and safety applications. Proc. SPIE 7671, 76710V (2010)

    Article  Google Scholar 

  13. Heinz, E., May, T., Born, D., Zieger, G., Anders, S., Zakosarenko, V., Schubert, M., Krause, T., Kruger, A., Schulz, M., Meyer, H.G.: Towards high-sensitivity and high-resolution submillimeter-wave video imaging. Proc. SPIE 8022, 802204 (2011)

    Article  Google Scholar 

  14. Gopalsami, N., Liao, S.L., Elmer, T.W., Koehl, E.R., Heifetz, A., Raptis, A.C., Spinoulas, L., Katsaggelos, A.K.: Passive millimeter-wave imaging with compressive sensing. Opt. Eng. 51, 091614 (2012)

    Article  Google Scholar 

  15. Watts, C.M., Shrekenhamer, D., Montoya, J., Lipworth, G., Hunt, J., Sleasman, T., Krishna, S., Smith, D.R., Padilla, W.J.: Terahertz compressive imaging with metamaterial spatial light modulators. Nat. Photonics 8, 605–609 (2014)

    Article  Google Scholar 

  16. Baraniuk, R.G.: Compressive sensing. IEEE Signal Process. Mag. 24, 118–121 (2007)

    Article  Google Scholar 

  17. Wakin, M.B., Laska, J.N., Duarte, M.F., Baron, D., Sarvotham, S., Takhar, D., Kelly, K.F., Baraniuk, R.G.: An architecture for compressive imaging. In: Presented at IEEE International Conference on Image Processing (ICIP 2006), USA, pp. 1273–1276 (2006)

  18. Parasoglou, P., Malioutov, D., Sederman, A.J., Rasburn, J., Powell, H., Gladden, L.F., Blake, A., Johns, M.L.: Quantitative single point imaging with compressed sensing. J. Magn. Reson. 201, 72–80 (2009)

    Article  Google Scholar 

  19. Chan, W.L., Charan, K., Takhar, D., Kelly, K.F., Baraniuk, R.G., Mittleman, D.M.: A single-pixel terahertz imaging system based on compressed sensing. Appl. Phys. Lett. 93, 121105 (2008)

    Article  Google Scholar 

  20. Demirci, S., Ozdemir, C.: Compressed sensing-based imaging of millimeter-wave ISAR data. Microw. Opt. Technol. Lett. 55, 2967–2972 (2013)

    Article  Google Scholar 

  21. Shen, H., Newman, N., Gan, L., Zhong, S.C., Huang, Y., Shen, Y.C.: Compressed terahertz imaging system using a spin disk, In: Presented at 35th International Conference On Infrared, Millimeter, And Terahertz Waves (IRMMW-THz 2010), Italy, (2010)

  22. Mota, J.F.C., Xavier, J.M.F., Aguiar, P.M.Q., Puschel, M.: Distributed basis pursuit. IEEE Trans. Sig. Proc. 60, 1942–1956 (2012)

    Article  MathSciNet  Google Scholar 

  23. Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53, 4655–4666 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  24. Gopalsami, N., Elmer, T.W., Liao, S., Ahern, R., Heifetz, A., Raptis, A.C., Luessi, M., Babacan, D., Katsaggelos, A.K.: Compressive sampling in passive millimeter-wave imaging. Proc. SPIE 8022, 80220I (2011)

    Article  Google Scholar 

  25. Smart, K., Du, J., Li, L., Wang, D., Leslie, K., Ji, F., Li, X.D., Zeng, D.Z.: A practical and portable solids-state electronic terahertz imaging system. Sensors 16, 579 (2016)

    Article  Google Scholar 

  26. Yang, J.R., Lee, W.J., Han, S.T.: Signal-conditioning block of a 1 \(\times \) 200 CMOS detector array for a terahertz real-time imaging system. Sensors 16, 319 (2016)

    Article  Google Scholar 

  27. Cotter, S.F., Rao, B.D., Engan, K., Kreutz-Delgado, K.: Sparse solutions to linear inverse problems with multiple measurement vectors. IEEE Trans. Sig. Proc. 53, 2477–2488 (2005)

  28. Zhang, Z., Rao, D.: Extension of sbl algorithms for the recoveryof block sparse signals with intra-block correlation. IEEE Trans. Sig. Proc. 61, 2009–2015 (2013)

    Article  Google Scholar 

  29. Duc-Son, P., Venkatesh, S.: Efficient algorithms for robust recovery of images from compressed data. IEEE Trans. Image Process. 22, 4724–4737 (2013)

    Article  MathSciNet  Google Scholar 

  30. Figueiredo, M.A.T., Nowak, R.D., Wright, S.J.: Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Signal Process. 1, 586–597 (2007)

    Article  Google Scholar 

  31. Lyu, Q., Lin, Z.C., She, Y.Y., Zhang, C.A.: Comparison of typical l(p) minimization algorithms. Neurocomputing 119, 413–424 (2013)

    Article  Google Scholar 

  32. Li, C.B., Yin, W.T., Jiang, H., Zhang, Y.: An efficient augmented Lagrangian method with applications to total variation minimization. Comput. Optim. Appl. 56, 507–530 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  33. Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans. Image Process. 18, 2419–2434 (2009)

    Article  MathSciNet  Google Scholar 

  34. Fadili M.J., Starck J.L.: Monotone operator splitting for optimization problems in sparse recovery. In: Presented at 2009 16th IEEE International Conference On Image Processing, Egypt, 1–6, pp. 1461–1464 (2009)

  35. Huggins, P.S., Zucker, S.W.: Greedy basis pursuit. IEEE Trans. Sig. Proc. 2007, 3760–3772 (2007)

    Article  MathSciNet  Google Scholar 

  36. Bi, X., Chen, X., Li, X., Leng, L.: Energy-based adaptive matching pursuit algorithm for binary sparse signal reconstruction in compressed sensing. Signal Image Video Process. 8, 1039–1048 (2014)

    Article  Google Scholar 

  37. Ramirez, C., Argaez, M.: An \(l_1\) minimization algorithm for non-smooth regularization. Signal Image Video Process. 9, 203–216 (2013)

    Google Scholar 

Download references

Acknowledgments

The authors thank Xiaoguang Tian, Pengsheng Wu, Kai Wang, Changlei Wang, Ting Li, Huakun Zhang and Tongshan Yuan for useful discussions on the algorithm and its applicability in real-life imaging systems. We give our sincere appreciations for the hard work of the reviewers and editors.

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Authors and Affiliations

  1. East China Research Institute of Electronic Engineering, Hefei, Anhui, China

    Hao Tu, Weihua Bu, Wenjing Wang, Bingxi Gao, Hui Feng & Shuai Wu

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  1. Hao Tu
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  2. Weihua Bu
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Correspondence to Hao Tu.

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Tu, H., Bu, W., Wang, W. et al. Applicability of Hadamard relaxation method to MMW and THz Imaging with compressive sensing. SIViP 11, 399–406 (2017). https://doi.org/10.1007/s11760-016-0974-6

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  • DOI: https://doi.org/10.1007/s11760-016-0974-6

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